AynCoding-Qwen2.5-Coder-1.5B-Instruct: Sovereign Epistemic Logic & Code Synthesis (Transformers Checkpoint)

AynCoding-Qwen2.5-Coder-1.5B-Instruct (enver/ayncoding-qwen2.5-coder-1.5b-instruct) is a full PyTorch / Safetensors Hugging Face transformers checkpoint for the sovereign Classical Arabic Logic (Mantiq) coding model.

It is loadable via standard AutoModelForCausalLM.from_pretrained("enver/ayncoding-qwen2.5-coder-1.5b-instruct") and compatible with all automated evaluation harnesses (lm-evaluation-harness, lighteval, vLLM, SGLang, and TGI).

Benchmark Highlights

Quick Start via Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "enver/ayncoding-qwen2.5-coder-1.5b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

prompt = "Build a thread-safe distributed rate limiter with Ghazalian invariants."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

License & Intellectual Property Rights

This model, its architecture (including BPE-Free Farāhīdian/Sībawayhian tokenization, RootAttention, and Ishtiqāq-Attention), and its weights are released under the AynEngine & University of Prishtina Sovereign Academic License.

• Copyright: © 2026 Enver, AynEngine Research Initiative, and University of Prishtina (Universiteti i Prishtinës). All rights reserved.
• Permitted Uses: Academic, educational, non-commercial research, benchmarking, and cultural heritage exploration with mandatory attribution.
• Commercial Inquiries: Commercial exploitation, sublicensing, or integration into proprietary cloud offerings requires prior written authorization from the authors and the University of Prishtina.
• Full License: See LICENSE in this repository.